AWS Machine Learning Blog

NOVEMBER 26, 2024

Neuron is the SDK used to run deep learning workloads on Trainium and Inferentia based instances. AWS AI chips, Trainium and Inferentia, enable you to build and deploy generative AI models at higher performance and lower cost. To get started, see AWS Inferentia and AWS Trainium Monitoring.

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AWS Machine Learning Blog

MAY 16, 2025

In this post, we share how Amazon Web Services (AWS) is helping Scuderia Ferrari HP develop more accurate pit stop analysis techniques using machine learning (ML). Since implementing the solution with AWS, track operations engineers can synchronize the data up to 80% faster than manual methods.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Choose Create stack.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment.

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Flipboard

MAY 28, 2025

Machine learning (ML) has emerged as a powerful tool to help nonprofits expedite manual processes, quickly unlock insights from data, and accelerate mission outcomesfrom personalizing marketing materials for donors to predicting member churn and donation patterns.

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NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. The data mesh architecture aims to increase the return on investments in data teams, processes, and technology, ultimately driving business value through innovative analytics and ML projects across the enterprise.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. API Gateway also provides a WebSocket API. These components are illustrated in the following diagram.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

Recognizing this need, we have developed a Chrome extension that harnesses the power of AWS AI and generative AI services, including Amazon Bedrock , an AWS managed service to build and scale generative AI applications with foundation models (FMs). The user signs in by entering a user name and a password.

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Dataconomy

APRIL 28, 2025

AWS SageMaker is transforming the way organizations approach machine learning by providing a comprehensive, cloud-based platform that standardizes the entire workflow, from data preparation to model deployment. What is AWS SageMaker?

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JANUARY 9, 2025

They provide various documents (including PAN and Aadhar) and a loan amount as part of the KYC After the documents are uploaded, theyre automatically processed using various artificial intelligence and machine learning (AI/ML) services. Prerequisites This project is built using the AWS Cloud Development Kit (AWS CDK).

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AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. Solution overview In active-passive mode, the SageMaker domain infrastructure is only provisioned in the primary AWS Region.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS), provide a performant and low cost framework to run LLMs efficiently in a containerized environment. Adjust the following configuration to suit your needs, such as the Amazon EKS version, cluster name, and AWS Region.

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JANUARY 14, 2025

In this post, we show how to extend Amazon Bedrock Agents to hybrid and edge services such as AWS Outposts and AWS Local Zones to build distributed Retrieval Augmented Generation (RAG) applications with on-premises data for improved model outcomes.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon SageMaker Studio is the first integrated development environment (IDE) purposefully designed to accelerate end-to-end machine learning (ML) development. These automations can greatly decrease overhead related to ML project setup, facilitate technical consistency, and save costs related to running idle instances.

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AWS Machine Learning Blog

OCTOBER 28, 2024

This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects.

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AWS Machine Learning Blog

DECEMBER 24, 2024

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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AWS Machine Learning Blog

JANUARY 31, 2025

Solution overview Our solution uses the AWS integrated ecosystem to create an efficient scalable pipeline for digital pathology AI workflows. Prerequisites We assume you have access to and are authenticated in an AWS account. The AWS CloudFormation template for this solution uses t3.medium

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AWS Machine Learning Blog

APRIL 29, 2025

InterVision Systems, LLC (InterVision), an AWS Premier Tier Services Partner and Amazon Connect Service Delivery Partner, has been at the forefront of this transformation, with their contact center solution designed specifically for city and county services called ConnectIV CX for Community Engagement.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

With the QnABot on AWS (QnABot), integrated with Microsoft Azure Entra ID access controls, Principal launched an intelligent self-service solution rooted in generative AI. Principal also used the AWS open source repository Lex Web UI to build a frontend chat interface with Principal branding.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

8B and 70B inference support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Trainium and Inferentia, enabled by the AWS Neuron software development kit (SDK), offer high performance and lower the cost of deploying Meta Llama 3.1 An AWS Identity and Access Management (IAM) role to access SageMaker.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge. Refer to Multi-User for instructions to create a user.

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AWS Machine Learning Blog

MAY 16, 2025

This post is co-authored by Manuel Lopez Roldan, SiMa.ai, and Jason Westra, AWS Senior Solutions Architect. Are you looking to deploy machine learning (ML) models at the edge? Designed to work on SiMas MLSoC (Machine Learning System on Chip) hardware, your models will have seamless compatibility across the entire SiMa.ai

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Flipboard

DECEMBER 3, 2024

Syngenta and AWS collaborated to develop Cropwise AI , an innovative solution powered by Amazon Bedrock Agents , to accelerate their sales reps’ ability to place Syngenta seed products with growers across North America. The collaboration between Syngenta and AWS showcases the transformative power of LLMs and AI agents.

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AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. Admins and users can also overwrite the defaults using the SDK defaults configuration file.

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AWS Machine Learning Blog

FEBRUARY 26, 2025

These experiences are made possible by our machine learning (ML) backend engine, with ML models built for video understanding, search, recommendation, advertising, and novel visual effects. By using sophisticated ML algorithms, the platform efficiently scans billions of videos each day.

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MAY 28, 2025

This post focuses on how the QP model used draft centric speculative decoding (SD)also called parallel decodingwith AWS AI chips to meet the demands of Prime Day. AWS AI chips and parallel decoding To overcome these challenges, Rufus adopted parallel decoding, a simple yet powerful technique for accelerating LLM generation.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of experience innovating with AI and machine learning at Amazon. The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3. If enabled, its status will display as Access granted.

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AWS Machine Learning Blog

FEBRUARY 6, 2025

Building upon a previous Machine Learning Blog post to create personalized avatars by fine-tuning and hosting the Stable Diffusion 2.1 We show how to then prepare the fine-tuned model to run on AWS Inferentia2 powered Amazon EC2 Inf2 instances , unlocking superior price performance for your inference workloads.

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Flipboard

JUNE 5, 2025

Amazon SageMaker AI provides a fully managed service for deploying these machine learning (ML) models with multiple inference options, allowing organizations to optimize for cost, latency, and throughput. AWS has always provided customers with choice. That includes model choice, hardware choice, and tooling choice.

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Data Science Dojo

OCTOBER 31, 2024

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Applied Machine Learning Scientist Description : Applied ML Scientists focus on translating algorithms into scalable, real-world applications.

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FEBRUARY 11, 2025

Enhancing AWS Support Engineering efficiency The AWS Support Engineering team faced the daunting task of manually sifting through numerous tools, internal sources, and AWS public documentation to find solutions for customer inquiries. Then we introduce the solution deployment using three AWS CloudFormation templates.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

You can try out the models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping provide data security.

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AWS Machine Learning Blog

JANUARY 15, 2025

Powered by generative AI services on AWS and large language models (LLMs) multi-modal capabilities, HCLTechs AutoWise Companion provides a seamless and impactful experience. Technical architecture The overall solution is implemented using AWS services and LangChain. AWS Glue AWS Glue is used for data cataloging.

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AWS Machine Learning Blog

OCTOBER 31, 2024

By integrating human annotators with machine learning, SageMaker Ground Truth significantly reduces the cost and time required for data labeling. Create the labeling job using the CreateLabelingJob API You can also create the custom labeling job programmatically by using the AWS SDK to invoke the CreateLabelingJob API.

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AWS Machine Learning Blog

APRIL 11, 2025

The AWS DeepRacer League is the worlds first autonomous racing league, open to anyone. Announced at re:Invent 2018, it puts machine learning in the hands of every developer through the fun and excitement of developing and racing self-driving remote control cars. Using a pre-trained Meta Llama 3.2

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JUNE 3, 2025

This post showcases how the TSBC built a machine learning operations (MLOps) solution using Amazon Web Services (AWS) to streamline production model training and management to process public safety inquiries more efficiently. It streamlines ML lifecycle management and stores model and pipeline artifacts.

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